| Adjusting the effect of nonstationarity in cluster-based and TFCE inference. | |
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MedLine Citation:
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PMID: 20955803 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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In nonstationary images, cluster inference depends on the local image smoothness, as clusters tend to be larger in smoother regions by chance alone. In order to correct the inference for such nonstationary, cluster sizes can be adjusted according to a local smoothness estimate. In this study, adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. We find that TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. We also find that adjusting for local smoothness via a separate resampling procedure is more effective at removing nonstationarity than an adjustment via a random field theory based smoothness estimator. |
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Authors:
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Gholamreza Salimi-Khorshidi; Stephen M Smith; Thomas E Nichols |
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Publication Detail:
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Type: Journal Article Date: 2010-10-16 |
Journal Detail:
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Title: NeuroImage Volume: 54 ISSN: 1095-9572 ISO Abbreviation: Neuroimage Publication Date: 2011 Feb |
Date Detail:
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Created Date: 2010-12-21 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9215515 Medline TA: Neuroimage Country: United States |
Other Details:
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Languages: eng Pagination: 2006-19 Citation Subset: IM |
Copyright Information:
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Copyright © 2010 Elsevier Inc. All rights reserved. |
Affiliation:
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Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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